plot.joint           package:JointModeling           R Documentation

_M_o_d_e_l _C_h_e_c_k_i_n_g

_D_e_s_c_r_i_p_t_i_o_n:

     Graphicals representation to help in model checking.

_U_s_a_g_e:

     ## S3 method for class 'joint':
     plot(x, comp = "mean", var = NULL, res = 'standard',
     which = 1:5, ask = nb.fig < length(which) && dev.interactive(), smooth =
     TRUE, ...)
     obs.vs.model(x.joint, plot.disp = FALSE, ...)
     rstand.vs.linpred(x, smooth = TRUE, ...)
     res.vs.explvar(x, var, res = 'standard', smooth = TRUE, ...)
     absres.vs.fitted(x, res = 'standard', smooth = TRUE, ...)
     adjvar.vs.linpred(x, smooth = TRUE, ...)
     qqglm(x, ...)

_A_r_g_u_m_e_n_t_s:

x,x.joint: A fitted GLM/GAM object or a fitted joint model.

    comp: The component for the joint model. Must be one of '"mean"' or
          '"disp"'.

     var: Character. The name of the explanatory variable selected.

     res: Character. Should be _standard_, _student_ of _brut_. The
          residual type considered.

   which: Numeric vector. Which plot must be produced.

     ask: Logical. If 'TRUE', user is asked before a new plot is
          produced.

  smooth: Logical. If 'TRUE' - the default, a smoothing curve is
          plotted thanks to the 'lowess' fucntion.

     ...: Optional parameters to be passed to 'plot', 'abline' and
          'qqnorm' functions.

plot.disp: Logical. Should the +/- standard deviation error bar be
          plottted around the point selected by the 'identify'
          function.

_D_e_t_a_i_l_s:

     The function 'plot.joint' is a special function to compute all the
     checking plots. Checking plots implemented are :

        1.  Observations functions of Fitted values

        2.  Standardized Residuals functions of Linear predictor

        3.  Residuals functions of an explanatory variable

        4.  Absolute Residuals functions of Fitted values

        5.  Adjusted Dependent Variable functions of Linear predictor

        6.  _QQ-plot_ of the residuals

     Several conclusions can be established thanks to these plots. One
     can refer to McCullagh and Nelder, 1987 for interpretation.

_V_a_l_u_e:

     Return a (several) graphic window(s).

_A_u_t_h_o_r(_s):

     Mathieu Ribatet and Bertrand Iooss

_R_e_f_e_r_e_n_c_e_s:

     McCullagh P. and Nelder J. A. (1987). _Generalized Linear Models
     Second Edition._ London: Chapman and Hall. ISBN 0-412-31760-5.

_E_x_a_m_p_l_e_s:

     X <- rnorm(500, 5)
     mu <- 2*X + 1
     sigma2 <- exp(-X)
     Y <- rnorm(500, mu, sqrt(sigma2))
     data.frm <- data.frame(Y=Y, X=X)
     ajust <- fitjoint("glm", 'Y~X', 'd~X', data = data.frm)
     layout(matrix(c(1,1,2,2,3,3,4,4,0,5,5,0),3,byrow=TRUE))
     plot(ajust, var = 'X')

